Common Era

Common Era (CE) and Before the Common Era (BCE) are year notations for the Gregorian calendar (and its predecessor, the Julian calendar), the world's most widely used calendar era. Common Era and Before the Common Era are alternatives to the original Anno Domini (AD) and Before Christ (BC) notations used for the same calendar era. The two notation systems are numerically equivalent: " CE" and "AD " each describe the current year; "400 BCE" and "400 BC" are the same year. The expression can be traced back to 1615, when it first appears in a book by Johannes Kepler as the annus aerae nostrae vulgaris (year of our common era), and to 1635 in English as "Vulgar Era". The term "Common Era" can be found in English as early as 1708, and became more widely used in the mid-19th century by Jewish religious scholars. Since the later 20th century, BCE and CE have become popular in academic and scientific publications because BCE and CE are religiously neutral terms. They are used by others who wish to be sensitive to non-Christians by not referring to Jesus, the center figure of Christianity, especially via the religious terms "Christ" and Dominus ("Lord") utilized by the other abbreviations. Anno Domini The idea of numbering years beginning from the date then believed to be the date of birth of Jesus, was conceived around the year 525 by the Christian monk Dionysius Exiguus. He did this to replace the then dominant Era of Martyrs system, because he did not wish to continue the memory of a tyrant who persecuted Christians. He numbered years from an initial reference date ("epoch"), an event he referred to as the Incarnation of Jesus. Dionysius labeled the column of the table in which he introduced the new era as "Anni Domini Nostri Jesu Christi". This way of numbering years became more widespread in Europe with its use by Bede in England in 731. Bede also introduced the practice of dating years before what he supposed was the year of birth of Jesus, and the practice of not using a year zero.

System Support for Robust Distributed Learning

Arsany Hany Abdelmessih Guirguis

Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...

EPFL2022

Extraction of Rare Earth Metals: The New Thermodynamic Considerations Toward Process Hydrometallurgy

Christian Ludwig, Rudolf Paul Wilhelm Jozef Struis, Andrea Testino, Ajay Bhagwan Patil

Successful management of secondary waste resources is essential for the viable circular economy. E-waste could serve as the potential urban mining source for the alternative supply chain of critical metals such as rare earth elements (REEs). The hydrometal ...

Springer Nature Switzerland AG2021

The SNP-Based Profiling of Montecristo Feral Goat Populations Reveals a History of Isolation, Bottlenecks, and the Effects of Management

System Support for Robust Distributed Learning

Extraction of Rare Earth Metals: The New Thermodynamic Considerations Toward Process Hydrometallurgy

System Support for Robust Distributed Learning

The SNP-Based Profiling of Montecristo Feral Goat Populations Reveals a History of Isolation, Bottlenecks, and the Effects of Management

Extraction of Rare Earth Metals: The New Thermodynamic Considerations Toward Process Hydrometallurgy